Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Ghiles3232/weckr-sdks --skill weckr-cost-estimatorgit clone --depth 1 https://github.com/Ghiles3232/weckr-sdksWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ghiles3232/weckr-sdks/weckr-cost-estimator)<a href="https://agentmods.dev/skills/ghiles3232/weckr-sdks/weckr-cost-estimator"><img src="https://agentmods.dev/badge/skills/ghiles3232/weckr-sdks/weckr-cost-estimator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/ghiles3232/weckr-sdks/weckr-cost-estimator"><img src="https://agentmods.dev/badge/skills/ghiles3232/weckr-sdks/weckr-cost-estimator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00097 | $0.01440 |
| Opus 5 | $0.00048 | $0.00720 |
| Sonnet 5 | $0.00019 | $0.00288 |
| Haiku 4.5 | $0.00010 | $0.00144 |
Grade A, and why
weckr-cost-estimator scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Weckr cost estimator
Turn an AI feature into a cost forecast before you ship it. Given the code for an LLM call, or a plain description of the feature, estimate the tokens per call, multiply by current model prices, and project the cost per call, per active user, and per month. Then sanity check that against what the user charges.
Estimates are forecasts, not invoices. Real cost depends on real prompts, real usage, and caching. Always give a range and state the assumptions. For the exact number in production, that is what Weckr measures.
When to use this skill
Use it when the question is forward looking about a specific feature:
- How much will this summarizer, chatbot, or agent cost to run.
- Can I afford to offer this on a $19 plan.
- What happens to cost if I switch from gpt-5.4 to gpt-5.4-mini, or to Claude Haiku.
- What per user monthly spend should I expect at 500 users.
For a pure price lookup use weckr-model-pricing. For auditing profitability across a whole set of pricing plans use weckr-margin-audit. For wiring real tracking into the app use weckr-integration.
How to estimate
Work through these steps and show them, so the user can challenge any assumption.
-
Find the call. From the code, identify the model, the system prompt, the user input, any retrieved or appended context, and the output cap (
max_tokens, or a typical response length). If there is no code, ask for or assume a rough shape and say so. -
Estimate tokens per call. Roughly 1 token is about 4 characters of English, or about 0.75 words. Sum the system prompt, user input, and context for input tokens; use the output cap or a typical length for output tokens. If a large system prompt or context repeats across calls and the provider caches it, price those tokens at the cached-input rate.
-
Price one call. Use current prices per million tokens (see the table below, or the
weckr-model-pricingskill for the full list):cost_per_call = (input_tokens / 1e6) * input_price + (output_tokens / 1e6) * output_price
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 84 lines · 97 tokens per session scan A 7e3f839f45ff
weckr-cost-estimator is a skill published in the GitHub repository Ghiles3232/weckr-sdks (8 stars, last pushed 18d ago), licensed MIT. It adds 97 tokens to every session and 1,440 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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